Distributed query execution and aggregation

ABSTRACT

Computer-implemented methods and systems are disclosed for receiving and indexing a plurality of files for later querying, for dynamically generating scripts to be executed during a query of a data store, and for horizontally distributing a query and aggregating results of the distributed query.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to co-pending U.S. patent application Ser. No. 17/727,764, filed Apr. 24, 2022 and titled “DATA INDEXING FOR DISTRIBUTED QUERY EXECUTION AND AGGREGATION”, which is hereby incorporated by reference in its entirety.

This application also discloses material disclosed within U.S. patent application Ser. No. 17/727,765, titled “DYNAMIC SCRIPT GENERATION FOR DISTRIBUTED QUERY EXECUTION AND AGGREGATION”, filed Apr. 24, 2022, and granted as U.S. Pat. No. 11,500,889, as well as material disclosed within U.S. patent application Ser. No. 17/727,767, titled “DISTRIBUTED QUERY EXECUTION AND AGGREGATION”, filed Apr. 24, 2022, and granted as U.S. Pat. No. 11,520,739. By virtue of their shared filing date, these publications are not admitted to be prior art with respect to the present application.

FIELD OF INVENTION

This disclosure relates to systems and methods for generating, structuring, and executing queries on a data store, and more specifically, to systems and methods for pre-processing a data query to improve efficiency, distributing the data query to a cloud of processors, and post-processing the results to respond to the query.

BACKGROUND

In the Structured Querying Language (SQL) or other relational database management systems (RDBMSs), there are a variety of functions that may be called, some of which return a single record (such as a SELECT on a field that acts as a unique key), some of which potentially return a multitude of records (such as a SELECT on a non-unique field, or on a JOIN of multiple tables), and some of which return single values derived from a data set (such as a COUNT of all records that match a criterion, or a SUM of all values in a particular column for records that match the criterion).

Most RDBMSs function very well even when querying tables with millions of records comprising terabytes of data. However, these solutions are highly dependent on proper indexing to avoid a need to iterate through every record to see if that record matches the query, and often fails to allow more sophisticated search techniques, such as search by regular expression (“regex”). Even with excellent indexing, existing solutions often break down when attempting to execute queries involving trillions of records comprising petabytes of data. Existing solutions for querying such massive data sets, such as Elasticsearch, may require massive clusters of servers and yet still have trouble effectively performing commands of greater sophistication, such as those requiring a JOIN of multiple tables, or those depending on partitioning of data.

As a result, there are advantages to having a system that provides speed and sophistication to big data queries, but doing so efficiently; avoiding the need for a proportional investment in hardware and achieving speed benefits beyond those that would result solely from increasing the number of computing clusters working in parallel.

SUMMARY OF THE INVENTION

A system for receiving and indexing a plurality of files for later querying is disclosed. The system comprises one or more data stores and non-transitory memory comprising instructions. When executed, the instructions cause processors to receive a plurality of files to be stored in the one or more data stores and store each file from the plurality of files in a bucket that exclusively stores files received during a predetermined window of time. The system periodically indexes a plurality of keys from the plurality of files by establishing a two-tiered index for each key, comprising an index dictionary and a Bloom filter, such that for every value of every key from the plurality of keys, there exists an entry in the index dictionary for that key indicating which files of the of plurality of files have that value for that key, and such that the Bloom filter for that key has that value inserted. Among other features, the Bloom filter allows a query to be received comprising a Boolean OR/AND/NOT of a value for a key, and if the value has not been inserted in the Bloom filter for that key, the query fails before downloading any of the plurality of files from the one or more data stores; for more efficient time-based file searching; and for the created index dictionary to be stored in the one or more data stores in a same format as the plurality of files, and even generated by the same query process that the indexing otherwise facilitates.

A system for dynamically generating scripts to be executed during a query of a data store is also disclosed. The system comprises a server comprising one or more processors and non-transitory memory comprising instructions. When executed by the processors, the instructions cause the processors to receive a query comprising a time window of files to be searched, key values to search on, and one or both of a filter selecting a subset of the files to be searched and an aggregation of data from all of the files or from all of the files that are filtered. In response, the system dynamically generates a script based on contents of the query, optimizing the script from a template to build up only features necessary to satisfy the query; and distributes the generated script horizontally to a plurality of computing devices that will execute the query by calling the generated script on each of the files to be searched. Various optimizations are provided in the dynamically generated script.

A system for distributing a query and aggregating results of the distributed query is also disclosed. The system comprises one or more data stores, a distribution server comprising one or more processors, a plurality of computing devices, each computing device being associated with at least one data store of the one or more data stores, and non-transitory memory comprising instructions. When executed, the instructions cause processors to receive a dynamically generated script representing a query of files in the one or more data stores and transmit the dynamically generated script to each computing device of the plurality of computing devices. The system directs each computing device of the plurality of computing devices to perform the query on a subset of files in the one or more data stores and receives from each computing device a subset of results based on execution of the dynamically generated script on the subset of files. The system may aggregate the subsets of results into a final result that represents a set of records that satisfy a query that was used to dynamically generate the script or that contain statistical summary information on records based on a query that was used to dynamically generate the script; and transmits the final result for storage and provision to a user who wrote the query.

BRIEF DESCRIPTION OF THE DRAWINGS

Other aspects, features and advantages will become more fully apparent from the following detailed description, the appended claims, and the accompanying drawings (provided solely for purposes of illustration without restricting the scope of any embodiment), of which:

FIG. 1 illustrates, in simplified form, a system of computing devices used to receive a query, pre-process the query, distribute the query, aggregate the results of the query, and return the results to a human user;

FIG. 2 depicts, in simplified form, a method of indexing and organizing files to facilitate future queries;

FIG. 3 depicts, in simplified form, a method of dynamically generating a script to be executed by each querying device;

FIG. 4 illustrates, in simplified form, a method of “enriching” queries and query results by associating a field's data in a machine-readable format with a human-preferred means of expressing the same data;

FIG. 5 illustrates, in simplified form, a method of distributing a script representing a query to a cluster of computing devices and each computing device performing the query;

FIG. 6 depicts, in simplified form, a method for processing the separate results of the distributed query to assemble them or otherwise prepare them to be forwarded to the end user;

FIG. 7 depicts, in simplified form, additional steps that are performed based on any options specified by the requesting user and in order to prepare the system to execute a new query; and

FIG. 8 is a high-level block diagram of a representative computing device that may be utilized to implement various features and processes described herein.

DETAILED DESCRIPTION

In order to address the limited querying functionality in prior art systems, a system is provided to facilitate queries across staggering quantities of data in a filesystem-like construct.

The data to be queried is preferably stored in a NOSQL format such as Apache Parquet, JavaScript Object Notation (JSON), or another columnar format that does not require a formal relational database construct with tables or external schema, and can be stored in a generic “data lake” without concern for organization, such as the storage services that might be provided by Amazon S3 or other cloud-based storage providers. Despite the lack of an SQL or SQL-like backend, the system allows an end user to create queries including SQL-like functions, such as select for a value in a column, select by regular expression being satisfied by a value in a column, group by value in a column, group by regex being satisfied a value in a column, joining on a column across two tabular formats, count of records having a value in a column, set of all unique values in a column, count of unique values present in a column, count of each value in a column, sum of values in a column, concatenation of fields with a delimiter, ratio of one column or sum of columns to another column or sum of columns, least value present in a column, greatest value present in a column, applying a filter to records that would otherwise be returned by a select, quartile statistics of the values in a column, median value in a column, mean value in a column, standard deviation of values in a column, and other statistics or manipulations of results. End users need not be familiar with any particular querying language such as SQL, nor with programming in general, but instead might need only understand the JSON format and fields of a custom querying language to be use within said format, or might even construct a query by using a web-based graphical user interface (GUI).

For example, the following JSON object can be used as a query to pull HTTP request records and determine a variety of valuable statistics for later analysis such as, for each IP address, the total number of requests, total bytes sent, first and most recent times requested, and so on during the previous day:

json_config = {  ″log_type″: ″http″,  ″begin″: int (1000*datetime.datetime(yesterday.year,yesterday.month,yest erday.day,0,0,0) .timestamp ( ) ) ,  ″end″: int(1000*datetime.datetime(yesterday.year,yesterday.month,yest erday.day,23,59,59,999999) .timestamp ( ) ) ,  ″s3_output_format″: ″multi″,  ″key″: {   ″columns″: [     { ″name″ : ″source_ip″, ″type″ : ″raw″ },     { ″name″ : ″dest_ip″, ″type″ : ″raw″ },     { ″name″ : ″dest_port″, ″type″ : ″raw″ },     { ″name″ : ″web_host″, ″type″ : ″raw″ },     { ″name″ : ″http_method″, ″type″ : ″raw″ }         ]    },  ″enrichments″: [   { ″name″ : ″q_ips_source_ip″, ″enrichment_source″:″q_ips″, ″enrichment_key″: ″source_ip″, ″enrichment_path″:″reference/q_latest_dagg.json″},  ],  ″output_columns″: [   { ″output _name″ : ″key″, ″type″: ″concat″, ″input_columns″: [″source_ip″, ″dest_ip″, ″dest_port″, ″web_host″, ″http_method″], ″delim″ : ″, ″},   { ″output_name″ : ″network_tuple″, ″type″: ″concat″, ″input_columns″: [″source_ip″, ″dest_ip″, ″dest_port″] , ″delim″ : ″, ″} ,   { ″output _name″ : ″local_dest″, ″input_column″: ″local_dest″, ″type″: ″pass_through″, ″preprocess″ : ″lower″},   { ″output_name″ : ″local_source″, ″input_column″: ″local_source″, ″type″: ″pass_through″, ″preprocess″ : ″lower″},   { ″output_name″ : ″session_count″, ″type″: ″count″} ,   { ″output_name″ : ″datetime″, ″input″: ″begin″, ″type″: ″metadata″},   { ″output_name″ : ″first_seen″, ″input_column″: ″datetime″, ″type″: ″least″},   { ″output_name″ : ″last_seen″, ″input_column″: ″datetime″, ″type″: ″greatest″},   { ″output_name″ : ″sensor″, ″input_column″: ″sensor″, ″type″: ″set″},   { ″output_name″ : ″http_status_code″, ″input_column″: ″http_status_code″, ″type″: ″set″, ″preprocess″ : ″forceint″},   { ″output_name″ : ″http_request_body_len″, ″input_column″: ″http_request_body_len″, ″type″: ″sum″ },   { ″output_name″ : ″http_response_body_len″, ″input_column″: ″http_response_body_len″, ″type″: ″sum″ }  ]  ,  ″filter_query″ : [   { ″subquery″: { ″is_q″ : { ″enrichment″: ″q_ips_source_ip″, ″values″: [None] } } }          ], }

After the user submits a query using a GUI or otherwise through an application programming interface (API), an optimized script is dynamically generated in Python (or a similar scripting language) based on the user query and on the features of the custom querying language that have been invoked by that particular query. The generated script is horizontally distributed to a cluster of computing devices, and executed by each in parallel. As a result of the horizontal scaling and parallel computation, trillions of records containing petabytes of data can be queried in a matter of minutes and useful data is provided to the user potentially while the user waits during a same session, instead of being processed and returned to the user hours or days later.

Depending on the nature of the query and the quantity of results, the results might be immediately provided to the end user via the GUI, might be transformed into a Parquet or JSON format and provided to the end user to download, might be routed to a separate destination for further processing, or might be saved in a format that allows recursive or iterative queries to be performed on the results of previous queries.

As a result of all of the above, an organization desiring to make a massive amount of data searchable and summarizable need only obtain a distributed storage medium and convert their data into the Parquet, JSON, or similar format, and immediately increase the ability of internal analysts to query the data and obtain statistics regarding that data. The ease of compressing Parquet and similar file formats without affecting any storage framework further increases the efficiency of the system.

Turning to the overall physical structure of the disclosed computing system, FIG. 1 illustrates, in simplified form, a system of computing devices used to receive a query, pre-process the query, distribute the query, aggregate the results of the query, and return the results to a human user.

As depicted in FIG. 1 , a user interface on a client computer 100 may be used to submit a query 105 via an API. The query 105 may, in a preferred embodiment, be typed into a text box in a web-based GUI and submitted by clicking a button that calls a JavaScript function to submit the query via the API. In other embodiments, more specialized software may take advantage of the API instead of a web browser; software may incorporate a dynamic linked library (DLL) that extends the API into a same language the software is written in; or the query definition may be submitted by other means, such as saving the query in a text file and uploading the text file.

The query 105 is preferably JSON based, with a number of required and optional fields. Required fields in a preferable embodiment include a beginning timestamp and an ending timestamp (to take advantage of time-based indexing and reduce the scope of a search), the columns to be included in the output of the query, the type of log files to limit the search to, the output format (single file or separate files), and either a key describing how to group data being obtained (for example, group by a particular field value or group all records matching a particular regex) or a filter (in order to retrieve full records matching a criterion instead of only obtaining some data from them). If a key has been provided, a filter may additionally be provided anyway to further refine the query.

For example, the following query could be used to search for all SMTP records of emails sent by an SMTP server on the date December 1, 2021, identify the sender email address of each email via a regex, and count the number of emails sent by each email address, using that address as a key for grouping the output:

{  ″log_type″: ″smtp″,  ″begin″: int(1000*datetime.datetime (2021,12,1,0,0,0) .timestamp( ) ),  ″end″: int(1000*datetime.datetime (2021,12,1,23,59,59,999999)      .timestamp ( ) ) ,  ″s3_output_format″: ″single″,  ″key″: {    ″columns″: [      { ″name ″ : ″smtp_mailfrom″       ″type″ : ″regex_group″,       ″regex″: ″.*?@ ( (.{1, } \. ) { 1, } [ {circumflex over ( )}>] { 1, } ) ″ }    ]  },  ″output_columns″: [    { ″output name″ : ″count″, ″type″: ″count″}  ] }

A web server 110 receives the query 105 via the API and may perform initial validation or optimization tasks on the query 105. For example, the data may be initially validated to confirm that it is in the JSON format and can be parsed, that it contains all required field names, that all field names are correct and contain no typographical errors, and so on. If there are errors in the structure of the query itself, they may be recoverable by replacing one form of punctuation with another or one misspelled word with a known querying keyword that appears to have been meant. If the columns included in the query key indicate that the query concerns potentially sensitive or legally protected data, the server 110 may also check user entitlements sent along with the query 105 to confirm that the user is permitted to access the data that would be returned by the query, and prevent the query from being performed if permission is absent or remove prohibited columns from the query while retaining permitted columns. If a required field is absent, it may nonetheless be possible to perform the query by including a default value for that field, such as setting the “end” to the present and the “beginning” to 24 hours previously, if beginning and ending timestamps are omitted from the query.

Any of the above checks or failsafes may result in a query that is somewhat changed from the initially submitted query, resulting in a validated query 115. In other embodiments, no changes may be performed, and the original query 105 may continue to be used without modification.

The validated query 115 or original query 105 is forwarded from the web server 110 to a queuing server 120 that tracks queries which are to be performed when a cluster of computing devices 135, or even multiple such clusters operating in parallel, that will actually perform the query are still busy with a previously submitted query. In a preferred embodiment, the Ray clustering solution is used to control each of the computing devices in unison and distribute tasks among them.

While waiting for the cluster of computing devices 135 to become available, a Python generation server 125 generates a custom Python script 130 to be used specifically for the validated query 115 or original query 105, based on the contents of that query, according to a method described further below in relation to FIG. 3 . In alternative embodiments, languages other than Python might be used, such as Perl, PHP, Ruby, or similarly interpreted scripting languages. In yet other embodiments, it is conceivable that a compiled language might be used to create an executable or interpretable program, such as C, C++, Java, etc.

When the cluster of computing devices 135 becomes available, the Python script 130 is forwarded to each of them, according to a method described further below in relation to FIG. 5 .

Each computing device 135 has access to at least a portion of the data storage 140. As previously mentioned, in a preferred embodiment, Amazon S3 acts in this capacity and provides the storage. The storage 140 is generally unstructured and can act as a “data lake” with various raw files 145 having different internal formats and significances, captured or produced by different sensors or software, indiscriminately inserted into the storage. Some indexing of the inserted files is performed, according to a method described further below in relation to FIG. 2 . Although, in a preferred embodiment, each of the files 145 is in the Apache Parquet format, the files could equally be stored in JSON format, or in another format that organizes data into one or more records, each with defined fields of data, such as a Comma Separated Value (CSV) database, some forms of Extensible Markup Language (XML), or other simple tabular or markup data formats. One advantage of allowing JSON input as an option is that the ultimate output of each query is by default in the JSON format, and this allows for recursive querying, a query that operates solely on the results of a previous query.

As each computing device 135 executes its instance of the Python script 130, it generates results 150 based on its portion of the storage 140. The portion of storage may be defined by limited access—for example, if the storage 140 is distributed across multiple locations and a computing device 135 only has access to one of these locations. The portion may instead, in a preferred embodiment, be the result of a division of a keyspace between the devices 135. For example, a key value in data may be hashed and assigned to a particular computing device 135 for processing based on the hash falling within a certain range assigned to that particular computing device 135. The outputs 150 from these scripts 130 may optionally be forwarded to a re-aggregation server or cluster of servers 155 to be combined and/or processed into a final result 160, according to a process described further below in relation to FIG. 6 . For example, executing the example query provided above (for tracking sent email counts), a first computing device searching a first region of storage may have discovered two emails sent by johndoe@example.com, and a second computing device searching a second region of storage may have discovered three more email sent by johndoe@example.com. In order to accurately provide a count on the requested key, the re-aggregation server or cluster of servers 155 would need to track counts for each key value in each result 150 and use them to generate results 160 showing that the email address had sent five emails during the query time window. For queries that do not involve mathematical operations to summarize or provide data statistics, such as a query for all records matching a filter, the re-aggregation server may simply generate a final result 160 that is the union or concatenation of all results 150 that were provided to it. In a preferred embodiment, however, no re-aggregation server is necessary. Instead, results 150 are stored in the storage 140 and made available for an end user to download and process in any order at the end user's convenience. This asynchronous and divided result may often be necessary when a query may take hours to be performed and return such a large dataset that the entire result cannot be stored in a single device's volatile memory at one time.

Ultimately, the individual results 150 or final results 160 are either directly transmitted to the request user's computer 100 or, preferably, are sent to the storage 140 to be obtained by the user at a later time or interacted with in other ways, such as by a query that acts exclusively on these results to further aggregate or generate statistics related to the initial query. In a preferred embodiment, the final result 160 is stored in the JSON format, though in other embodiments, other formats might be used, such as Parquet, XML, plaintext, or other formats suitable to store and present the requested information. In a preferred embodiment, the results format is JSON or a similar compatible format to enable a recursive process of executing a first query, then executing a second query operating on the output generated by the first query, further aggregating or refining results.

Although a particular division of functions between devices is described with relation to the systems depicted in FIG. 1 , above, other configurations are possible in which functions are divided among devices differently. For example, some or all of the functions ascribed to the user's computing device 100, the web server 110, the queueing server 120, the Python generation server 125, and the re-aggregation server 155 could conceivably be performed by a single device with multiple threads executing different software modules simultaneously.

Alternatively, each system or device from among the web server 110, the queueing server 120, the Python generation server 125, and the re-aggregation server 155 may in fact be a cluster of computing devices sharing functionality for concurrent processing. Further, although these various computing elements are described as if they are one computing device or cluster each, a cloud-based solution with multiple access points to similar systems that synchronize their data and are all available as backups to one another may be preferable in some embodiments to a unique set of computing devices all stored at one location. The specific number of computing devices and whether communication between them is network transmission between separate computing devices or accessing a local memory of a single computing device is not so important as the functionality that each part has in the overall scheme. What does remain of importance is that there are multiple computing devices 135 in a cluster to distribute the query among them and re-aggregate the results after each device has produced its individual result.

Data Ingestion and Indexing

Although indexing of all files is not necessary for a query to be performed, lack of an index will require every file in the storage 140 to be independently checked for possible satisfaction of selection and filtering criteria provided by the user.

FIG. 2 depicts, in simplified form, a method of indexing and organizing files to facilitate future queries.

In a preferred embodiment, the storage 140 is organized into a series of “buckets” such that all files received during a given hour of a given day go into the same bucket, to facilitate time-based queries. Additional sub-buckets may be provided within each bucket based on file type or log type, source of the file, a value of a commonly accessed column, or other criteria. Further, buckets based on a particular unit or interval of time may be further sub-divided into more granular units of time, such as sub-buckets for each hour within a day bucket, or sub-buckets for each minute in an hour bucket. Each file stored is given a unique identifier to be used within the index.

Whenever a new file is received (Step 200), the system checks whether enough time has elapsed for a new bucket to be created (Step 205). As previously mentioned, this is, in a preferred embodiment, after one hour has passed since the last new time-based bucket was added, though shorter or longer lengths of time may be used for each bucket window if there are higher or lower volumes of data to be indexed. If the time has elapsed, a new bucket is created (Step 210).

In either event, the file is inserted into the most recently created bucket and the system may also determine any relevant sub-bucket (Step 215) as described above, based on file type or log type, source of the file, a value of a commonly accessed column, or other criteria.

In some embodiments, indexes may be regularly generated by a distributed query, according to the methods described above and below. For example, in order to index every email sent by an SMTP server by sender or by recipient, a query to group every email by sender or recipient and output a column that is a concatenation of every unique ID of every email that was within that group would result in an index that could be consulted in future queries by sender or recipient. In a preferred embodiment, the generated indexes are themselves stored in the same storage 140 as the underlying data for the sake of simplicity. In other embodiments, it may be desirable to have a specialized fast retrieval storage for the index to further improve efficiency.

In a preferred embodiment, these generated indexes are generated once per day, such that the software checks whether the current indexes are more than 24 hours old (Step 220) and if so, begins to generate the new indexes. The preferred interval of one day corresponds to the preferred interval bucket size, so that each newly generated index is relevant to the contents of the newest bucket. If a different bucket size, such as an hour or a week is used, a different indexing frequency may be preferred as well.

At the same time that each value of each key column (or a numerical offset that can be used to look up the value in an array) is inserted into the dictionary-based index, that value or offset is also inserted into a Bloom filter for that key (Step 225) that tracks whether a given value for a key has been indexed. Although a Bloom filter always has a small possibility of returning a false positive and reporting that a value has been inserted when it actually was not, the inability to have a false negative and the fast lookup time on a Bloom filter allows for a search on for a key value that is not present to short circuit even faster and fail even before consulting the dictionary table of key values.

In a preferred embodiment, each index, both the dictionary and Bloom filter portions, is horizontally distributed (Step 230) to ensure that each computing device 135 has access to the index.

Although the time-based checks whether to add new buckets to the storage (Step 205) and whether to generate indexes (Step 220) are conceptually described as if they are triggers called when a file is received, various embodiments might schedule a cron job or other process to perform these tasks at the intended time independent of insertions into storage.

Query Preprocessing and Python Generation

Once all data has been inserted into storage (and, if it will be indexed, that has been completed as well), the system can begin performing queries for records or data aggregations of that data.

FIG. 3 depicts, in simplified form, a method of dynamically generating a script to be executed by each querying device.

Initially, the query is checked (Step 300) for any contents that may interfere with insertion into a Python template (or template written in another scripting language), such as quotes or other special characters that may need to be escaped with backslashes or removed; use of Python keywords that may need to be quoted, escaped, or removed; or any other feature that may cause unexpected behavior when inserted into a script that will be interpreted and executed.

The required time window (both beginning and end) is used to select a set of buckets to which the search will be limited when the script is executed (Step 305). Because it is possible that files or logs gathered by a sensor may have been delayed somewhat, additional buckets may be added despite the intention that they represent windows of time that do not overlap with the query window. For example, if logs are to be searched that arrived between midnight and 1 a.m., the additional buckets for 1 a.m. to 2 a.m. and 2 a.m. to 3 a.m. might be included in case data that was originally generated between midnight and 1 a.m. did not arrive for ingestion until after 1 a.m. Depending on the source of data, any intermediate processing it may have undergone, and especially any human element delaying the transmission of data, the search window may be arbitrarily expanded further to reduce the risk of failing to find desired data.

Based on what output types and/or aggregations the query will involve, the script is built up (Step 310) to include only features that will be necessary to accomplish the task. For example, a generic, pre-built solution would likely require a tree of if/else statements or case switches to test whether an output field is to be a sum (and if so, perform the addition), an average (and if so, perform the addition and division), etc. In contrast, a dynamically generated script can have code to sum or average if and only if those features are used in the query, without any conditional control structures being necessary. When said code is being executed millions or billions of times during performance of the query, the lack of unnecessary conditional structures leads to a marked improvement in performance.

In some embodiments, additional features may be incorporated into the script to be executed as it is run. For example, a key might be defined in the query as, rather than the value of a particular column, a Python function (or function in another programming language) called upon the value of a column, allowing for even greater sophistication in a query. Such a function might include a decryption function, allowing the raw data to be encrypted before storage and only decrypted at the moment the file is retrieved from storage and checked to see whether it satisfies the query. Although in theory any arbitrary function might be allowed, in a preferred embodiment, a number of capabilities often enabled by programming languages (such as accessing a filesystem or input/output devices) may be prevented, and instead have functions limited to those that only operate on variables in memory.

Although a sequence is implied by the above steps, the steps may logically be performed independently and in virtually any order, or even simultaneously through parallel processing.

After the script has been fully built, the resulting Python file is preferably saved to storage 140 (Step 315) to await the computing devices 135 becoming available to execute the script on the various files in storage. Other optimizations, such as distribution of specific functions from the Python file to specific devices in a Ray cluster, may also be performed.

As a result of the dynamic script generation that only incorporates features that are foreseen as necessary from the structure of the query, object orientation and more complex design patterns are unnecessary. Instead, the resultant script merely contains whatever if/else structures and for loops are necessary to obtain the information requested by the query, with few function calls and little overhead.

The generated script contains code to attempt to take advantage of Boolean logic to short-circuit when a query is definitely satisfied or will definitely not be satisfied. For example, if there are two filters with a Boolean OR between them, the Python script will be written such that the first one to be checked is a key that is indexed, and if it is found to be present, will terminate and return the record without checking the other. If there are two filters with a Boolean AND between them, the Bloom filter part of the index may be checked first for a value of an indexed key, and if the value is not present in the Bloom filter, the search can immediately be terminated. Moreover, if there is an AND aspect to a query and any of the keys are not indexed, use of the index may be abandoned altogether, as it will be ultimately necessary to download and check each file sequentially, regardless.

When multiple filters are to be used, they are preferably expressed in disjunctive normal form, i.e., a set of sub-filters with an implicit OR disjunction between them. Thus, if it is desired to obtain all records for which “A” and “B” are columns with a true value, one would include the single sub-query as follows:

  ″filter_query″ : [        { ″subquery″: {″A″: { ″values″: [True] },               ″B″: { ″values″: [True] }              }        }       ] whereas, if it is desired to obtain all records for which “A” or “B” are columns with a true value, one would use multiple sub-queries:

  ″filter_query″ : [        {″subquery″: { ″A″: { ″values″: [True] } } },        {″subquery″: { ″B″: { ″values″: [True] } } }       ]

The generated code may contain a variety of checks that will be performed at runtime when the query is executed, in order to optimize performance based on the results of the initial query. For example, if a query only requests a count of records satisfying a filter, the index may be consulted to count the records in the dictionary and satisfy the query without downloading any files. If a query is for a time window that is so recent the data has not yet been indexed, or if the query does not operate on any indexed fields, all use of the indexes may be abandoned and each file sequentially checked for possible satisfaction of the query. In some circumstances, the time window overlaps both a period when indexing was completed and a period when indexing was not completed—for example, a window for the previous seven days, but indexes for the present day have not been created and only exist for data between seven days and one day old. In those circumstances, a hybrid approach will be followed where the indexes are consulted in searching through the older data, but every file among the newer data is sequentially checked.

When the script actually downloads a file to verify that it satisfies a query, all fields in the key or filter of the query are looped through and examined in sequence. Because Parquet has native support for data comparison features such as string comparison, values being less than or greater than a criterion, etc., wherever possible, elements of the query are converted into calls to Parquet's API for checking values and filtering.

For all fields in the key or filter that are not implementable via Parquet itself, code is included in the script to parse the data from Parquet and to check whether the field has an appropriate value to satisfy the query.

Finally, since Parquet does not have native support for regular expressions, any regexes used as filters may be included. These regexes can be precompiled at an earlier stage in order to avoid being compiled each time the script is run on each individual file.

Enrichment Query Preprocessing

Although the basic query syntax permits joins between two data sources that share a key, in some cases, necessary data for enabling a functionality similar to SQL's table joining may be stored in a data source external to the primary data sources being queried. For example, a log of packets sent over a network may only contain IP addresses, but a query might want to examine statistics regarding packets associated with users or departments of an organization. Accordingly, there is value in “enriching” the packet logs by combining the packet log data with a data source that associates each IP address with a user and/or department. This enrichment can be performed in any situation where a same key is shared across multiple data sources.

FIG. 4 illustrates, in simplified form, a method of “enriching” queries and query results by associating a field's data in a machine-readable format with a human-preferred means of expressing the same data.

Enrichment is achieved by regularly generating a table of translations from a value to one or more associated values and storing them in the storage 140 to be accessed by the computing devices 135 or the re-aggregation server 155 at the time of executing the query. In a preferred embodiment, Enrichment JSONs are generated via a daily cron job or similar scheduled software process (Step 400).

After generation, the JSON (or other file format) is propagated to the storage such that it is available to each computing device (Step 405), enabling those computing devices to perform pseudo-joins without communication with any other external source or database.

When a user specifies a query, the query may have key, filtering, or output fields include one or more predefined enrichment fields (Step 410). In a preferred embodiment, the user may use a UI to select an enrichment field from a list of available enrichment fields, and doesn't need to know the “join” syntax that would be required to achieve similar functionality in an SQL database system with multiple interrelated tables.

For example, the following JSON query might be generated:

json_config = {  ″user″ : ″″,  ″log_type″: ″external/auditd″,  ″begin″: int(1000*datetime.datetime (yesterday.year,yesterday.month,yest erday.day,0,0,0) .timestamp ( ) ),  ″end″: int(1000*datetime.datetime (yesterday.year,yesterday.month,yest erday.day,0,59,59) .timestamp ( ) ) ,  ″s3_output_format″: ″single″,  ″key″: {   ″columns″: [    { ″enrichment″: ″grn_hostname″, ″enrichment_value″: ″grn″}   ]  },  ″enrichments″: [   { ″name″ : ″grn_hostname″, ″enrichment_source″: ″grn″, ″enrichment_path″: ″reference_data/internal/host_server_enrichment/host_server_en richment_latest.json″, ″enrichment_key″: ″hostname″} ,  ],  ″output_columns″: [   { ″output_name″ : ″count″, ″type″: ″count″} ,  ] }

The above JSON might be used to search for files having a hostname of “grn” and aggregate their values, even though the underlying files actually only contain IP addresses, and not hostnames.

To achieve this functionality, the data from the enrichment JSON (associating a particular IP address with a particular hostname) is automatically joined into a raw data structure with fields loaded from a Parquet file, even before the Python script begins to parse it (Step 415). As a result, the script is completely unaware that a given file did not contain the enriched information in its raw format.

Horizontal Distribution and Execution

Once a dynamic script has been generated and is ready to be executed, it must be distributed so that it can be acted on in parallel and apply the script to as many files as possible, as quickly as possible.

FIG. 5 illustrates, in simplified form, a method of distributing a script representing a query to a cluster of computing devices and each computing device performing the query.

Initially, using the Ray cluster manager, the cluster of computing devices 135 is locked (Step 500) to ensure that they only work on one query at a time.

The next pending query is selected from the query server (Step 505) and its associated dynamically generated script is retrieved (Step 510)

In a preferred embodiment, the cluster of computing devices 135 contains 256 actual machines or 256 virtual machines distributed among a number of actual machines capable of multi-core simultaneous processing. Files from the storage 140 are preferably assigned (Step 515) to each of the computing devices 135 in a “round robin” style and/or by chunking the set of files into portions of equal size and assigning them sequentially to each of the computing devices, so that each computing device 135 receives an approximately equal 1/256share of the files. In other embodiments, other schemes of division may be used, such as hashing files such that, if the first byte of the hash of the file represents the number N from 0 to 255, the N+1th machine or processor will retrieve that file and determine whether it satisfies the query. If fewer than 256 processors are capable of acting, files may be assigned by the value of the hash mod the number of processors; if more than 256 processors are desired, the files could be assigned based on the first 9 bits, 10 bits, etc., of the hash instead of only the first 8 bits. So long as an appropriate hashing function is used, files should be evenly distributed between the set of processors.

Each machine, for each file, executes the dynamic script (Step 520) upon that file, and tracks the output of the script for each file. If the query is returning summary or statistical data rather than records, a dictionary is created (Step 525) and used to create an entry for each value for each key in the query and updated as each file is parsed, until all files have been parsed (Step 530).

Post Processing and Aggregation

FIG. 6 depicts, in simplified form, a method for processing the separate results of the distributed query to assemble them or otherwise prepare them to be forwarded to the end user.

In general, every query will either request in response a union set of all records that satisfy the query, or instead request derived or assembled statistical data on each value for one or more key columns. For an example of the latter, the following query might be used to determine the total number of connection attempts made to each destination IP address during the previous 24-hour period:

json_config = {  ″user″ : ″″,  ″log_type″: ″conn″,  ″begin″: int(1000*datetime.datetime (yesterday.year,yesterday.month,yest erday.day,0,0,0) .timestamp ( ) ) ,  ″end″: int(1000*datetime.datetime (yesterday.year,yesterday.month,yest erday.day,0,59,59) .timestamp ( ) ) ,  ″s3_output_format″: ″single″,  ″key″: {     ″columns″: [      { ″name″ : ″dest_ip″, ″type″ : ″raw″ },     ]    },  ″output_columns″: [   { ″output_name″ : ″count″, ″type″: ″count″} ], [″filter_query″ : [          { ″subquery″: { ″local_dest″: { ″values″: [True] }        ] }

When each of the computing devices 135 has obtained its intermediate results 150, they may each be provided to a re-aggregation server or cluster of servers 155 (Step 600) for combination into final results 160. If the re-aggregation functionality is being provided by a cluster of servers, it is important that work be partitioned to the servers in a deterministic way that ensures all data to be aggregated for a given key value is assigned to the same server, so that aggregation functions such as sum, max, or average have access to the full relevant data set associated with that given key value before applying the function. For example, if the numbers of incoming connections are to be summed for each IP address over a window of time, there is little value in assigning log entries randomly throughout the cluster to be summed, rather than assigning all the entries associated with a first IP address to a first server, and all entries associated with a second IP address to a second server. The partitioning may be performed by hashing the key value for each result and forwarding it to the Nth server of the cluster, where N is the hash, mod the number of servers.

The combination step acts essentially as a map reduce, applying a particular function to each of the sets of results, sorted by the key value. Alternatively, there may be no further re-aggregation required by the query, and the intermediate results 150 may be provided individually for storage or use by the requesting user.

If the output options include enrichments that have not yet been applied to the data, enrichments are applied (Step 605) to augment query results with additional data from external sources. This can allow even more sophisticated queries that operate on the enriched data. A significant power of data enrichment is that the enriched values can themselves be values or keys in other tabular data or unstructured data. To extend the previous example, if an IP address is being enriched to include a hostname associated with the IP address, a second data source might be organized by hostname and contain additional data that can now be associated with each IP address during aggregation. With these cross-datatype enrichments, it is possible to simulate the inner or outer join functionality provided by SQL (allowing two tables storing different data types to be joined for purposes of a query) without requiring storage of data in an SQL-compliant format. Another potential benefit of enriched data is that the data might be augmented with a more human-friendly data format or human-discernible significance. For example, in output containing IP addresses, the human user, machine name, or other useful information about the use of a given IP address might be added to aid a human user—who presumably has not memorized the IP addresses of machines in a particular network—to interpret the query results.

Once the required fields are all present in their final format, depending on the nature of the query (Step 610), any statistics are calculated (Step 615) or the full set of records are assembled (Step 620) from the intermediate results 150.

If the desired statistics are as simple as a count or sum, a tracking variable may be used while iterating through each of the intermediate results 150. In some embodiments, more exotic data calculations might be performed than merely using a variable to track a sum, minimum, maximum, etc. as the results are iterated. For example, a query might request a classification of data matching filters into five classifications, without knowing in advance what the nature of the output would be. The re-aggregation server 155 might, in response, perform a k-means classification of the output for k=5 and create a new field in the data output for each record's classification.

Finally, the individual partial results 150 or final result 160 is stored (Step 625) in the storage 140 so that it can be accessed by the end user or by future scripts executing a recursive query upon query results.

In a preferred embodiment, the aggregation process uses fixed software rather than dynamically generated software as in the querying step. Efficiency gains from dynamic software at this step are somewhat diminished because the scope of the data to process has already been reduced by orders of magnitude after the distributed queries have been performed. Nevertheless, in some embodiments, it might be preferable to dynamically generate code to perform the aggregation of data results when the functions used or the type of data being aggregated could benefit from optimized code that lacks if statements or loops that will never be necessary.

Resolution

When all processing is complete and the query has been fully resolved, some basic housekeeping is performed and the results are made useful to the end user.

FIG. 7 depicts, in simplified form, additional steps that are performed based on any options specified by the requesting user and in order to prepare the system to execute a new query.

First, various statistics may be gathered (Step 700) concerning the execution of the query, such as total time, the number of files downloaded, number of records provided, and so on. These statistics may be saved for future analysis or provided to the end user along with the query results.

Next, the lock on the Ray cluster is removed to enable the cluster to process a new query (Step 705). The queueing server is notified (Step 710) that the lock has been removed and that code related to a new query can be sent to the Ray cluster.

The web server 110 is also notified that the process has been completed, to allow any web-based UI to indicate that the query is complete (Step 715). Alternatively, in embodiments lacking a user interface, the end user or the end user's software may be responsible for periodically checking the storage to determine if new files for storing results have been created and have been unlocked after writing is complete.

Depending on the nature of the results, any UI may also be updated to include all or a portion of the results (Step 720). For example, if a query only requests a count or other single statistic of all records that satisfy a query, that statistic may simply be displayed on the screen. If a query requests counts to be aggregated for each value of a particular key column, the UI may be configured to display the first five, or first one hundred, etc. key values, ordered in descending order by count numbers. If the output has been saved to an external file to which the user has access, a hyperlink or other information needed to obtain the file may be displayed via the UI.

Computing Devices And Software

Although FIG. 1 depicts a preferred configuration of computing devices and software modules to accomplish the software-implemented methods described above, those methods do not inherently rely on the use of any particular specialized computing devices, as opposed to standard desktop computers and/or web servers. For the purpose of illustrating possible such computing devices, FIG. 8 , below, describes various enabling devices and technologies related to the physical components and architectures described above.

FIG. 8 is a high-level block diagram of a representative computing device that may be utilized to implement various features and processes described herein, for example, the functionality of the user's computing device 100, the web server 110, the queueing server 120, the Python generation server 125, the re-aggregation server 155, each distributed computing device 135, or any other computing device described. The computing device may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types.

As shown in FIG. 8 , the computing device is illustrated in the form of a special purpose computer system. The components of the computing device may include (but are not limited to) one or more processors or processing units 800, a system memory 810, and a bus 815 that couples various system components including memory 810 to processor 800.

Bus 815 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Processing unit(s) 800 may execute computer programs stored in memory 810. Any suitable programming language can be used to implement the routines of particular embodiments including C, C++, Java, assembly language, etc. Different programming techniques can be employed such as procedural or object oriented. The routines can execute on a single computing device or multiple computing devices. Further, multiple processors 800 may be used.

The computing device typically includes a variety of computer system readable media. Such media may be any available media that is accessible by the computing device, and it includes both volatile and non-volatile media, removable and non-removable media.

System memory 810 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 820 and/or cache memory 830. The computing device may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system 840 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically referred to as a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus 815 by one or more data media interfaces. As will be further depicted and described below, memory 810 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments described in this disclosure.

Program/utility 850, having a set (at least one) of program modules 855, may be stored in memory 810 by way of example, and not limitation, as well as an operating system, one or more application software, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment.

The computing device may also communicate with one or more external devices 870 such as a keyboard, a pointing device, a display, etc.; one or more devices that enable a user to interact with the computing device; and/or any devices (e.g., network card, modem, etc.) that enable the computing device to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interface(s) 860.

In addition, as described above, the computing device can communicate with one or more networks, such as a local area network (LAN), a general wide area network (WAN) and/or a public network (e.g., the Internet) via network adaptor 880. As depicted, network adaptor 880 communicates with other components of the computing device via bus 815. It should be understood that although not shown, other hardware and/or software components could be used in conjunction with the computing device. Examples include (but are not limited to) microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may use copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It is understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 

What is claimed is:
 1. A system for distributing a query and aggregating results of the distributed query, comprising: one or more data stores; a distribution server comprising one or more processors; a first one or more querying computing devices, each computing device being associated with at least one data store of the one or more data stores; a second one or more aggregating computing devices; and non-transitory memory comprising instructions that, when executed by the one or more processors of the distribution server, cause those processors to: receive a dynamically generated script representing a query of files in one or more data stores; transmit the dynamically generated script to each computing device of a first one or more querying computing devices; direct each computing device of the first one or more querying computing devices to perform the query on a subset of files in the one or more data stores; receive from each computing device a subset of results based on execution of the dynamically generated script on the subset of files, wherein each subset of results represents a set of records that satisfy a query that was used to dynamically generate the script or that contain statistical summary information on records based on a query that was used to dynamically generate the script; direct each computing device of a second one or more aggregating computing devices to aggregate the subsets of results by a key value; and transmit aggregated results for storage and provision to a user who wrote the query or another user.
 2. The system of claim 1, wherein the subsets of files are assigned to the first one or more querying computing devices in a round robin fashion.
 3. The system of claim 1, wherein a value of a hash of the key value is used to determine which computing device of the second plurality of aggregating computing devices is directed to aggregate results related to that key value.
 4. The system of claim 1, wherein the statistical summary information is a count of numbers of files that satisfy the query, grouped by value of a provided key field.
 5. The system of claim 1, wherein the statistical summary information is a sum of values, count of values, standard deviation of values, or other statistical property of values in a provided key field, grouped by value of another key field.
 6. The system of claim 1, wherein a k-means clustering is performed during aggregation of the subsets of results to generate a classifying field in output of the query.
 7. The system of claim 1, wherein the query comprises one or more enrichment fields specifying replacement of values from a key field in the query with a replacement value from an external data source.
 8. A computer-implemented method for distributing a query and aggregating results of the distributed query, comprising: receiving a dynamically generated script representing a query of files in one or more data stores; transmitting the dynamically generated script to each computing device of a first one or more querying computing devices; directing each computing device of the first one or more querying computing devices to perform the query on a subset of files in the one or more data stores; receiving from each computing device a subset of results based on execution of the dynamically generated script on the subset of files, wherein each subset of results represents a set of records that satisfy a query that was used to dynamically generate the script or that contain statistical summary information on records based on a query that was used to dynamically generate the script; directing each computing device of a second one or more aggregating computing devices to aggregate the subsets of results by a key value; and transmitting aggregated results for storage and provision to a user who wrote the query or another user.
 9. The computer-implemented method of claim 8, wherein the subsets of files are assigned to the first one or more querying computing devices in a round robin fashion.
 10. The computer-implemented method of claim 8, wherein a value of a hash of the key value is used to determine which computing device of the second one or more aggregating computing devices is directed to aggregate results related to that key value.
 11. The computer-implemented method of claim 8, wherein the statistical summary information is a count of numbers of files that satisfy the query, grouped by value of a provided key field.
 12. The computer-implemented method of claim 8, wherein the statistical summary information is a sum of values, count of values, standard deviation of values, or other statistical property of values in a provided key field, grouped by value of another key field.
 13. The computer-implemented method of claim 8, wherein a k-means clustering is performed during aggregation of the subsets of results to generate a classifying field in output of the query.
 14. The computer-implemented method of claim 8, wherein the query comprises one or more enrichment fields specifying replacement of values from a key field in the query with a replacement value from an external data source. 